Summary of Linked Adapters: Linking Past and Future to Present For Effective Continual Learning, by Dupati Srikar Chandra et al.
Linked Adapters: Linking Past and Future to Present for Effective Continual Learning
by Dupati Srikar Chandra, P. K. Srijith, Dana Rezazadegan, Chris McCarthy
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to address the problem of catastrophic forgetting in deep learning models during continual learning. The authors suggest using pre-trained models with task-specific adapters to adapt to new tasks while retaining knowledge from previous ones. However, existing approaches have limitations, including not transferring knowledge across tasks effectively. To overcome this challenge, the proposed Linked Adapters method utilizes a weighted attention mechanism to model both forward and backward knowledge transfer between task-specific adapters. The authors demonstrate the effectiveness of their approach through experiments on various image classification datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about helping machines learn new things without forgetting what they already know. Right now, big computers can’t do this very well because they get “stuck” on one new thing and forget all the old stuff. The researchers came up with a clever way to help these computers remember everything by using special connections between different tasks. They tested their idea on lots of pictures and it worked really well! This means that in the future, we might be able to use machines that can learn about many things without forgetting what they already know. |
Keywords
» Artificial intelligence » Attention » Continual learning » Deep learning » Image classification
